---
title: "Motor Vehicle Accidents in Victoria"
output: 
  flexdashboard::flex_dashboard:
        vertical_layout: scroll
        orientation: rows
        source_code: embed
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE,
                      eval = TRUE,
                      message = FALSE, 
                      warning = FALSE)
```

```{r libraries}
library(flexdashboard)
library(tidyverse)
library(lubridate)
library(janitor)
library(knitr)
library(kableExtra)
library(ggmap)
library(ggthemes)
library(plotly)
```

```{r load-data}
accidents <- read_csv("data/ACCIDENT.csv") %>%
  clean_names()

locations <- read_csv("data/ACCIDENT_LOCATION.csv") %>%
  clean_names()

nodes <- read_csv("data/NODE.csv") %>%
  clean_names()

persons <- read_csv("data/PERSON.csv") %>%
  clean_names()

vehicles <- read_csv("data/VEHICLE.csv") %>%
  clean_names()
```

```{r create-year-hour-day}
accidents <- accidents %>%
  mutate(accidentdate = dmy(accidentdate),
         Year = year(accidentdate),
         Hour = hour(accidenttime),
         Weekday = wday(accidentdate, 
                        label = TRUE,
                        abbr = FALSE))
```

Part 3 {data-icon="fa-battery-three-quarters"}
===================================== 

Row {.tabset data-height=800}
---------

### **Accidents Map**

```{r accidents-map}
location_box <- c(min(nodes$long), 
                  min(nodes$lat), 
                  max(nodes$long),
                  max(nodes$lat))

location_map <- get_map(location = location_box, 
                        source = 'osm')

  ggmap(location_map) + 
    geom_point(data = nodes,
               aes(x = long,
                   y = lat), 
               colour="#0072B2", 
               alpha=0.5, 
               size = 0.05) +
    theme_map()
```

### **Accidents by Gender**

```{r accidents-gender-table}
persons <- persons %>%
  mutate(sex = recode(sex,
                      "F" = "Female",
                      "M" = "Male",
                      "U" = "Unknown"))
```

```{r accidents-gender-plot}
persons %>% 
  filter(road_user_type_desc == "Drivers",
         sex %in% c("Female",
                    "Male"),
         age > 15) %>% 
  count(age,
        sex,
        name = "Accidents") %>% 
  
  ggplot(aes(x = age,
             y = Accidents, 
             color = sex)) +
  geom_line() +
  xlab("Age")
  
ggplotly()
```

### **Death Rate by User Type**

```{r user-death-rate}
user_type <- persons %>% 
  count(road_user_type_desc, 
        name = "User")

user_type_fatal <- persons %>% 
  filter(inj_level_desc == "Fatality") %>% 
  count(road_user_type_desc, 
        name = "Fatal")

user_type %>% 
  left_join(user_type_fatal) %>% 
  mutate(death_rate = Fatal / User) %>% 
  
  ggplot(aes(x = fct_reorder(road_user_type_desc, 
                             death_rate), 
             y = death_rate)) +
  geom_col(fill = "#999999") +
  labs(x = "User Type",
       y = "Death Rate") +
  theme(axis.text.x = element_text(angle = 45,
                                   hjust = 1))
  
```

### **Roads with Most Accidents and Highest Death Rates**

```{r join-road-names}
roads_accidents <- accidents %>%
  left_join(locations) %>% 
  mutate(Road = paste(road_name,
                      road_type))
```

```{r accidents-by-road}
accidents_per_road <- roads_accidents %>%
  count(Road, 
        name = "Accidents"
        ,sort = T)

deaths_per_road <- roads_accidents %>%
  group_by(Road) %>%
  tally(no_persons_killed,
        name = "Deaths",
        sort = TRUE)

accidents_per_road %>% 
  head(8) %>% 
  kable(caption = "Accidents by road") %>% 
  kable_styling(bootstrap_options = "striped")
```

```{r deadliest-road}
deadliest_road <- accidents_per_road %>%
  left_join(deaths_per_road) %>%
  filter(Accidents >= 100) %>%
  mutate(Deaths_per_accident = Deaths/Accidents) %>%
  arrange(desc(Deaths_per_accident))

deadliest_road %>% 
  head(8) %>% 
  kable(caption = "Deadliest roads") %>% 
  kable_styling(bootstrap_options = "striped")
```

Column {.sidebar data-width=350}
----

***

> **Findings**

1. The accidents are concentrated around Melbourne and branch out as we get further away. Small concentrations are also present around other cities and towns

2. Males commit more accidents than females. Both commit the highest number of accidents as new drivers at a young age, the accidents steadily decrease as the age increases

3. Pedestrians are the biggest casualty of accidents with the highest number of fatalities. Bicyclists are the least at risk of death

4. Regional highways are the deadliest types of roads

***